diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..04f1616433e405776e82500167f034899a28de14 100644 --- a/module2/exo2/exercice.ipynb +++ b/module2/exo2/exercice.ipynb @@ -1,5 +1,49 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean 14.11\n", + "Min 2.80\n", + "Max 23.40\n", + "Median 14.50\n", + "Std 4.33\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "data = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])\n", + "\n", + "print('Mean {:.2f}'.format(data.mean()))\n", + "print('Min {:.2f}'.format(data.min()))\n", + "print('Max {:.2f}'.format(data.max()))\n", + "print('Median {:.2f}'.format(np.median(data)))\n", + "print('Std {:.2f}'.format(np.std(data, ddof=1)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +60,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..91285ec35439867621952e81073a7b2f1b5d94f2 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,14 +9,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek" + "import isoweek\n", + "import os\n", + "import urllib.request" ] }, { @@ -28,13 +30,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", + "data_file = \"incidence.csv\"\n", + "\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" ] }, { @@ -61,11 +65,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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22020093110696102066.0119326.0168155.0181.0FRFrance
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52020063206669195481.0217857.0314297.0331.0FRFrance
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1020200133685031608.042092.05648.064.0FRFrance
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1620194731866914759.022579.02822.034.0FRFrance
1720194631603012567.019493.02419.029.0FRFrance
182019453101387160.013116.01510.020.0FRFrance
19201944378225010.010634.0128.016.0FRFrance
20201943394876448.012526.0149.019.0FRFrance
21201942377475243.010251.0128.016.0FRFrance
22201941371224720.09524.0117.015.0FRFrance
23201940385055784.011226.0139.017.0FRFrance
24201939370914462.09720.0117.015.0FRFrance
25201938348972891.06903.074.010.0FRFrance
26201937331721367.04977.052.08.0FRFrance
2720193632295728.03862.031.05.0FRFrance
28201935310102.02018.020.04.0FRFrance
2920193431672279.03065.031.05.0FRFrance
.................................
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1846 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202011 3 101704 93652.0 109756.0 154 142.0 \n", + "1 202010 3 104977 96650.0 113304.0 159 146.0 \n", + "2 202009 3 110696 102066.0 119326.0 168 155.0 \n", + "3 202008 3 143753 133984.0 153522.0 218 203.0 \n", + "4 202007 3 183610 172812.0 194408.0 279 263.0 \n", + "5 202006 3 206669 195481.0 217857.0 314 297.0 \n", + "6 202005 3 187957 177445.0 198469.0 285 269.0 \n", + "7 202004 3 122331 113492.0 131170.0 186 173.0 \n", + "8 202003 3 78413 71330.0 85496.0 119 108.0 \n", + "9 202002 3 53614 47654.0 59574.0 81 72.0 \n", + "10 202001 3 36850 31608.0 42092.0 56 48.0 \n", + "11 201952 3 28135 23220.0 33050.0 43 36.0 \n", + "12 201951 3 29786 25042.0 34530.0 45 38.0 \n", + "13 201950 3 34223 29156.0 39290.0 52 44.0 \n", + "14 201949 3 25662 21414.0 29910.0 39 33.0 \n", + "15 201948 3 22367 18055.0 26679.0 34 27.0 \n", + "16 201947 3 18669 14759.0 22579.0 28 22.0 \n", + "17 201946 3 16030 12567.0 19493.0 24 19.0 \n", + "18 201945 3 10138 7160.0 13116.0 15 10.0 \n", + "19 201944 3 7822 5010.0 10634.0 12 8.0 \n", + "20 201943 3 9487 6448.0 12526.0 14 9.0 \n", + "21 201942 3 7747 5243.0 10251.0 12 8.0 \n", + "22 201941 3 7122 4720.0 9524.0 11 7.0 \n", + "23 201940 3 8505 5784.0 11226.0 13 9.0 \n", + "24 201939 3 7091 4462.0 9720.0 11 7.0 \n", + "25 201938 3 4897 2891.0 6903.0 7 4.0 \n", + "26 201937 3 3172 1367.0 4977.0 5 2.0 \n", + "27 201936 3 2295 728.0 3862.0 3 1.0 \n", + "28 201935 3 1010 2.0 2018.0 2 0.0 \n", + "29 201934 3 1672 279.0 3065.0 3 1.0 \n", + "... ... ... ... ... ... ... ... \n", + "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 166.0 FR France \n", + "1 172.0 FR France \n", + "2 181.0 FR France \n", + "3 233.0 FR France \n", + "4 295.0 FR France \n", + "5 331.0 FR France \n", + "6 301.0 FR France \n", + "7 199.0 FR France \n", + "8 130.0 FR France \n", + "9 90.0 FR France \n", + "10 64.0 FR France \n", + "11 50.0 FR France \n", + "12 52.0 FR France \n", + "13 60.0 FR France \n", + "14 45.0 FR France \n", + "15 41.0 FR France \n", + "16 34.0 FR France \n", + "17 29.0 FR France \n", + "18 20.0 FR France \n", + "19 16.0 FR France \n", + "20 19.0 FR France \n", + "21 16.0 FR France \n", + "22 15.0 FR France \n", + "23 17.0 FR France \n", + "24 15.0 FR France \n", + "25 10.0 FR France \n", + "26 8.0 FR France \n", + "27 5.0 FR France \n", + "28 4.0 FR France \n", + "29 5.0 FR France \n", + "... ... ... ... \n", + "1816 59.0 FR France \n", + "1817 64.0 FR France \n", + "1818 97.0 FR France \n", + "1819 93.0 FR France \n", + "1820 80.0 FR France \n", + "1821 116.0 FR France \n", + "1822 149.0 FR France \n", + "1823 281.0 FR France \n", + "1824 395.0 FR France \n", + "1825 485.0 FR France \n", + "1826 544.0 FR France \n", + "1827 689.0 FR France \n", + "1828 722.0 FR France \n", + "1829 762.0 FR France \n", + "1830 926.0 FR France \n", + "1831 1113.0 FR France \n", + "1832 1236.0 FR France \n", + "1833 832.0 FR France \n", + "1834 459.0 FR France \n", + "1835 207.0 FR France \n", + "1836 190.0 FR France \n", + "1837 198.0 FR France \n", + "1838 224.0 FR France \n", + "1839 266.0 FR France \n", + "1840 219.0 FR France \n", + "1841 176.0 FR France \n", + "1842 163.0 FR France \n", + "1843 195.0 FR France \n", + "1844 308.0 FR France \n", + "1845 213.0 FR France \n", + "\n", + "[1846 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data_file, skiprows=1)\n", "raw_data" ] }, @@ -364,7 +1335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/incidence.csv b/module3/exo1/incidence.csv new file mode 100644 index 0000000000000000000000000000000000000000..6b3a9d382144c654404ae5ad3e387f8a8d7b2ce7 --- /dev/null +++ b/module3/exo1/incidence.csv @@ -0,0 +1,1848 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202011],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2020-05-13T16:04:02+02:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name +202011,3,101704,93652,109756,154,142,166,FR,France +202010,3,104977,96650,113304,159,146,172,FR,France +202009,3,110696,102066,119326,168,155,181,FR,France +202008,3,143753,133984,153522,218,203,233,FR,France +202007,3,183610,172812,194408,279,263,295,FR,France +202006,3,206669,195481,217857,314,297,331,FR,France +202005,3,187957,177445,198469,285,269,301,FR,France +202004,3,122331,113492,131170,186,173,199,FR,France +202003,3,78413,71330,85496,119,108,130,FR,France +202002,3,53614,47654,59574,81,72,90,FR,France +202001,3,36850,31608,42092,56,48,64,FR,France +201952,3,28135,23220,33050,43,36,50,FR,France +201951,3,29786,25042,34530,45,38,52,FR,France +201950,3,34223,29156,39290,52,44,60,FR,France +201949,3,25662,21414,29910,39,33,45,FR,France +201948,3,22367,18055,26679,34,27,41,FR,France +201947,3,18669,14759,22579,28,22,34,FR,France +201946,3,16030,12567,19493,24,19,29,FR,France +201945,3,10138,7160,13116,15,10,20,FR,France +201944,3,7822,5010,10634,12,8,16,FR,France +201943,3,9487,6448,12526,14,9,19,FR,France +201942,3,7747,5243,10251,12,8,16,FR,France +201941,3,7122,4720,9524,11,7,15,FR,France +201940,3,8505,5784,11226,13,9,17,FR,France +201939,3,7091,4462,9720,11,7,15,FR,France +201938,3,4897,2891,6903,7,4,10,FR,France +201937,3,3172,1367,4977,5,2,8,FR,France +201936,3,2295,728,3862,3,1,5,FR,France +201935,3,1010,2,2018,2,0,4,FR,France +201934,3,1672,279,3065,3,1,5,FR,France +201933,3,1593,68,3118,2,0,4,FR,France +201932,3,1592,71,3113,2,0,4,FR,France +201931,3,2337,695,3979,4,2,6,FR,France +201930,3,1944,415,3473,3,1,5,FR,France +201929,3,2096,420,3772,3,0,6,FR,France +201928,3,1195,125,2265,2,0,4,FR,France +201927,3,2466,848,4084,4,2,6,FR,France +201926,3,1798,402,3194,3,1,5,FR,France +201925,3,1544,389,2699,2,0,4,FR,France +201924,3,876,17,1735,1,0,2,FR,France +201923,3,2004,748,3260,3,1,5,FR,France +201922,3,2181,602,3760,3,1,5,FR,France +201921,3,2585,1102,4068,4,2,6,FR,France +201920,3,2891,1202,4580,4,1,7,FR,France 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+198450,3,123680,101401,145959,225,184,266,FR,France +198449,3,101073,81684,120462,184,149,219,FR,France +198448,3,78620,60634,96606,143,110,176,FR,France +198447,3,72029,54274,89784,131,99,163,FR,France +198446,3,87330,67686,106974,159,123,195,FR,France +198445,3,135223,101414,169032,246,184,308,FR,France +198444,3,68422,20056,116788,125,37,213,FR,France diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..5763072abe5a553e281f0b191b7880492d1cf9ed 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,253 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "\n", + "url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", + "data = pd.read_csv(url, skiprows=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-100:].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "weeks = [pd.Period(pd.Timestamp(y, 9, 1), 'W') for y in range(1991, sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "52\n", + "53\n", + "52\n", + "52\n", + "52\n", + "52\n", + "52\n" + ] + } + ], + "source": [ + "x = []\n", + "y = []\n", + "for week1, week2 in zip(weeks[:-1],\n", + " weeks[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " print(len(one_year))\n", + " \n", + " x.append(week2.year)\n", + " y.append(one_year.sum())\n", + " \n", + "serie = pd.Series(index=x, data=y)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "serie.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +264,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..e79f42fa9a83156c587fcb7952ee225f3361752e 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,418 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estimating network latency and capacity from asymmetric measures\n", + "\n", + "We first load our dependencies and download the provided datasets if required." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "from os import path\n", + "from urllib.request import urlretrieve\n", + "import gzip\n", + "\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "lan_url = 'http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/liglab2.log.gz'\n", + "lan_file = 'liglab2.log.gz'\n", + "\n", + "if not path.exists(lan_file):\n", + " urlretrieve(lan_url, lan_file)\n", + " \n", + "wan_url = 'http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/stackoverflow.log.gz'\n", + "wan_file = 'stackoverflow.log.gz'\n", + "\n", + "if not path.exists(wan_file):\n", + " urlretrieve(wan_url, wan_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## First dataset: Local Area Network conditions\n", + "\n", + "### Data extraction\n", + "\n", + "The first dataset contains ping results. Here is an extract of the provided data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "b'[1421761682.052172] 665 bytes from lig-publig.imag.fr (129.88.11.7): icmp_seq=1 ttl=60 time=22.5 ms\\n'\n", + "b'[1421761682.277315] 1373 bytes from lig-publig.imag.fr (129.88.11.7): icmp_seq=1 ttl=60 time=21.2 ms\\n'\n", + "b'[1421761682.502054] 262 bytes from lig-publig.imag.fr (129.88.11.7): icmp_seq=1 ttl=60 time=21.2 ms\\n'\n", + "b'[1421761682.729257] 1107 bytes from lig-publig.imag.fr (129.88.11.7): icmp_seq=1 ttl=60 time=23.3 ms\\n'\n", + "b'[1421761682.934648] 1128 bytes from lig-publig.imag.fr (129.88.11.7): icmp_seq=1 ttl=60 time=1.41 ms\\n'\n" + ] + } + ], + "source": [ + "with gzip.open(lan_file, 'rb') as f:\n", + " for _ in range(5):\n", + " print(next(f))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create a function to parse these log lines and test it." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "def parse_ping_line(l):\n", + " assert len(l) > 0\n", + " assert l[0] == '['\n", + " \n", + " timestamp = pd.Timestamp(float(l[1:l.index(']')]), unit='s')\n", + " size = np.nan\n", + " rtt = np.nan\n", + " \n", + " tokens = l.split(' ')\n", + " for i, t in enumerate(tokens):\n", + " if t == 'bytes':\n", + " assert np.isnan(size)\n", + " size = int(tokens[i-1])\n", + " elif t.startswith('time='):\n", + " rtt = float(t[len('time='):])\n", + " \n", + " return [timestamp, size, rtt]\n", + " \n", + "test_line = '[1421761682.052172] 665 bytes from lig-publig.imag.fr (129.88.11.7): icmp_seq=1 ttl=60 time=22.5 ms\\n'\n", + "[timestamp, size, rtt] = parse_ping_line(test_line)\n", + "assert timestamp == pd.Timestamp('2015-01-20 13:48:02.052172')\n", + "assert size == 665\n", + "assert rtt == 22.5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now process our full dataset and check if any data is missing." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " size rtt\n", + "timestamp \n", + "2015-01-20 13:48:04.770828000 21 NaN\n", + "2015-01-20 13:48:26.146320000 9 NaN\n", + "2015-01-20 13:49:08.647974000 9 NaN\n", + "2015-01-20 13:49:48.500515999 9 NaN\n", + "2015-01-20 13:50:05.331125000 18 NaN\n" + ] + } + ], + "source": [ + "rows = []\n", + "with gzip.open(lan_file, 'rb') as f:\n", + " for l in f:\n", + " try:\n", + " rows.append(parse_ping_line(l.decode()))\n", + " except (AssertionError):\n", + " print('invalid line', l)\n", + "\n", + "df = pd.DataFrame(rows, columns=['timestamp', 'size', 'rtt']).set_index('timestamp').sort_index()\n", + "print(df[df.isnull().any(axis=1)].head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The value `time` is missing from some rows. We remove these measurements." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " size rtt\n", + "count 44036.000000 44036.000000\n", + "mean 1000.025729 5.136663\n", + "std 573.659666 12.221312\n", + "min 24.000000 1.000000\n", + "25% 499.000000 1.270000\n", + "50% 989.000000 1.440000\n", + "75% 1499.000000 2.310000\n", + "max 2007.000000 276.000000" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.dropna().copy()\n", + "df.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data analysis\n", + "\n", + "We plot the rtt along time axis, with different levels of zooms." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(y='rtt', title='full dataset')\n", + "df[:1000].plot(y='rtt', title='zoom over first 1000 points')\n", + "df[150:300].plot(y=['rtt', 'size'], title='zoom over first observed spikes of latency')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can observe spikes of latency (rtt) in the dataset.The last plot suggests that the packet size **is not** the only parameter that affects latency.\n", + "\n", + "We now study the relation between packet size and rtt." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df.plot(x='size', y='rtt', kind='scatter')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We \"zoom\" over the discontinuity ($size \\approx 1480$)." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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